Pioneering Approaches in Data Management

The business landscape is evolving rapidly, and with that comes a massive amount of data that organizations must manage. However, many professionals and researchers need help to leverage this data effectively, as there is a lack of comprehensive guidance on integrating data analytics into management practices. Pioneering Approaches in Data Management bridges this gap by providing a framework that combines theoretical concepts with practical applications, empowering readers to use data analytics to its fullest potential. This book is an essential resource for researchers, educators, and practitioners who want to understand the transformative power of data analytics. It delves into cutting-edge methodologies, tools, and case studies to provide fresh insights into how data analytics can drive decision-making and innovation across various sectors. By emphasizing real-world applications and case studies, this publication offers a deeper understanding of how data analytics can be integrated into management strategies, shaping the future of research and practice in this rapidly evolving field. Designed for academic scholars, students, and business professionals, Pioneering Approaches in Data Management offers practical insights and comprehensive guidance on the latest developments in data analytics. It explores topics such as big data's impact on strategic decision-making, machine learning in management, and data-driven marketing strategies, equipping readers with the tools and knowledge needed to navigate the complexities of data analytics and drive organizational success in the age of big data and analytics.

Kanak Kalita is prominent professor and researcher in Computational Engineering, acknowledged among the Top 2% of scientists in 2023 by the Elsevier-Scopus and Stanford University listing. He earned both his M.E and Ph.D. in Applied Mechanics from Indian Institute of Engineering, Science & Technology, Shibpur, India in 2014 and 2019 respectively. He currently holds the position of Associate Professor in the Department of Mechanical Engineering at the Vel Tech University, Chennai. Dr. Kalita's scholarly output is both prolific and impactful, as evidenced by his authorship of over 75+ SCI and 130+ SCOPUS articles. His editorial expertise is also notable, having edited 8 book volumes. He serves on the editorial boards of several esteemed journals such as Scientific Reports, Frontiers in Mechanical Engineering, and the SAE International Journal of Materials and Manufacturing. His guest editorships for numerous journals further demonstrate his commitment to advancing scholarly discourse in his field. His work is highly regarded in the academic community, as reflected by his impressive 1800+ citations and an h-index of 24. Dr. Kalita is an engaging speaker, having delivered over 20 expert lectures across various academic and professional platforms. He has successfully guided eight undergraduate, four postgraduate students and is currently supervising four Ph.D. candidates. His research interests include machine learning, fuzzy decision making, metamodeling, process optimization, the finite element method, and composites. His ORCID id is 0000-0001-9289-9495.

Xiao-Zhi Gao received his B.Sc. and M.Sc. degrees from the Harbin Institute of Technology, China in 1993 and 1996, respectively. He obtained his D.Sc. (Tech.) degree from the Helsinki University of Technology (now Aalto University), Finland in 1999. He has been working as a professor at the University of Eastern Finland, Finland since 2018. Prof. Gao has published more than 400 technical papers in refereed journals and international conferences. His current Google Scholar H-index is 31. His research interests are nature-inspired computing methods with their applications in optimization, data mining, machine learning, control, signal processing, and industrial electronics.

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